def reichardt_bornholdt(network, t_step=0.9, **kwargs):
"""
Produce a clustering of the network represented by the adjacency
matrix `adjacency`.
Parameters
----------
network: clustering.Network
A Network object representing an adjacency matrix
Returns
-------
A Series representing the community membership
"""
clustering_c.set_globals(network, g=25)
communities = clustering_c.cluster(network, **kwargs)
communities = pd.DataFrame(np.array(communities), columns=['community'])
communities['node'] = network.adjacency.index
return communities
for x in binary]
objective = pd.Series(
{str(c.values):hessian(adjacency, c.values) for c in community_lists})
return objective.order(ascending=False)
if __name__ == "__main__":
# A network with two very obvious communities:
# A, B, C, D and E, F, G, H. A single connection
# from E to D joins the two communities
rows = [[0,1,1,0,0,0,0,0],
[1,0,1,1,0,0,0,0],
[0,1,0,0,0,0,0,0],
[1,1,0,0,0,0,0,0],
[0,0,0,1,0,1,1,0],
[0,0,0,0,1,0,0,1],
[0,0,0,0,0,0,0,1],
[0,0,0,0,1,1,0,0],
]
names = ["A", "B", "C", "D", "E", "F", "G", "H"]
adj = pd.DataFrame(rows).astype(float)
adj.index = names
adj.columns = names
a = clustering_c.Network(adj)
test_communities = pd.Series([0,0,0,0,0,0,0,0], index=names)
modularity = _brute_force(a, clustering_c.modularity)
potts = _brute_force(a, clustering_c.reichardt_bornholdt)
clustering_c.test()
clustering_c.cluster(a)